PELE scores: Pelvic X-ray Landmark Detection by Pelvis Extraction and Enhancement
This work addresses a domain-specific problem for medical imaging by improving landmark detection in pelvic X-rays to aid in computer-assisted diagnosis and treatment of pelvic diseases, though it is incremental as it builds on prior methods.
The authors tackled the problem of landmark detection in pelvic X-rays by proposing a PELE module that uses 3D CT anatomical knowledge to isolate the pelvis, eliminating soft tissue interference, and achieved state-of-the-art performance on datasets totaling 850 images.
The pelvis, the lower part of the trunk, supports and balances the trunk. Landmark detection from a pelvic X-ray (PXR) facilitates downstream analysis and computer-assisted diagnosis and treatment of pelvic diseases. Although PXRs have the advantages of low radiation and reduced cost compared to computed tomography (CT) images, their 2D pelvis-tissue superposition of 3D structures confuses clinical decision-making. In this paper, we propose a PELvis Extraction (PELE) module that utilizes 3D prior anatomical knowledge in CT to guide and well isolate the pelvis from PXRs, thereby eliminating the influence of soft tissue. We conduct an extensive evaluation based on two public datasets and one private dataset, totaling 850 PXRs. The experimental results show that the proposed PELE module significantly improves the accuracy of PXRs landmark detection and achieves state-of-the-art performances in several benchmark metrics, thus better serving downstream tasks.